asthma_map

library(tidyverse)
library(janitor)
library(haven)
library(plotly)
library(readr)
library(rjson)

##Creating state level brfss map of asthma prevalence

#Loading the main brfss data from local library
state_df <- read_csv("brfss_data/brfss_12072021.csv")

#Have to use state abbreviations to work with the plotly map
state_df <- state_df %>%
  mutate(state = ifelse(state == 66, NA, state)) %>%
  drop_na() %>%
  mutate(state = factor(state, labels = c("AL", 'AK', "AZ", "AR", "CA", "CO", "CT", "DE", "DC", "FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", 'PR', "RI", "SC", 'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'VI', 'WA', 'WV', 'WI', 'WY')))

#Creating an asthma prevalence variable
state_df <- state_df %>%
  group_by(state, iyear) %>%
  count(asthma_current) %>%
  mutate(asthma_perc = (n/sum(n) * 100), asthma_perc = round(asthma_perc, 2)) %>%
  filter(asthma_current == "yes")

#Creating hover label for map
state_df <- state_df %>%
  mutate(hover = paste0(state, ": ", asthma_perc))

#Creating the state level asthma prevalence map in plotly
geo_properties = list(
  scope = "usa",
  projection = list(
    type = "albers usa"),
  showland = TRUE,
  landcolor = toRGB('white'))

asthma_graph <- state_df %>%
  plot_geo(
    locationmode = 'USA-states',
    frame = ~iyear) %>%
  add_trace(locations = ~state,
            z = ~asthma_perc,
            zmin = 0,
            zmax = 15,
            text = ~hover,
            hoverinfo = 'text',
            color = ~asthma_perc,
            colorscale = "Viridis") %>%
  layout(geo = geo_properties,
         font = "DM Sans",
         title = "Asthma Prevalence in the United State, 2013-2021") %>%
  colorbar(title = "Asthma Prevalence") %>%
  animation_slider(
    currentvalue = list(prefix = "YEAR "))

##Creating a map of asthma prevalence by urban county by year in plotly #First load the brfss_cities dataset from local brfss library

cities_df <- read_csv("brfss_data/brfss_cities.csv")

#create a prevalence variable for asthma_current and then make a hover label

cities_df <- cities_df %>%
  group_by(fips, brfss_year, county, state) %>%
  count(asthma_current) %>%
  mutate(asthma_perc = (n/sum(n) * 100), asthma_perc = round(asthma_perc, 2)) %>%
  filter(asthma_current == "yes") %>%
  ungroup()

cities_df <- cities_df %>%
  mutate(hover = paste0(county, ", ", state, ": ", asthma_perc, "%"),
         nfips = sprintf("%05d", fips))

#Load county level map from the below link
url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'

counties <- rjson::fromJSON(file=url)


#Creating the map
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white'))

urban_graph <- cities_df %>% 
  plot_ly(
    frame = ~brfss_year
  ) %>% 
  add_trace(
    type="choropleth",
    geojson=counties,
    locations = cities_df$nfips,
    z = cities_df$asthma_perc,
    colorscale="Viridis",
    zmin=0,
    zmax=15,
    text = ~hover,
    hoverinfo = "text",
    marker=list(line=list(
      width=0))) %>%
  layout(geo = g,
         font = "DM Sans",
         title = "Asthma Prevalence in US Urban Counties, 2013-2019") %>%
  colorbar(title = "Asthma Prevalence") %>%
  animation_slider(
    currentvalue = list(prefix = "YEAR "))

The two maps!

urban_graph

asthma_graph

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A work by Tara Ahi, Gunnar Colleen, Mandy Miles, Jodi scharf, and © Copyright 2021 The Project Team, All Rights Reserved | Powered by Github.io